IBM SPSS modeler essentials : effective techniques for building powerful data mining and predictive analytics solutions /

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IBM SPSS Modeler allows quick, efficient predictive analytics and insight building from your data, and is a popularly used data mining tool. This book will guide you through the data mining process, and presents relevant statistical methods which are used to build predictive models and conduct other...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Salcedo, Jesus (Autor), McCormick, Keith (Consultant) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham (England) : Packt Publishing, 2017.
Temas:
SPSS (Computer file)
Social sciences > Statistical methods > Data processing.
Data mining.
Data Mining
Sciences sociales > Méthodes statistiques > Informatique.
Exploration de données (Informatique)
Data capture & analysis.
Information architecture.
Database design & theory.
COMPUTERS > General.
Computers > Data Processing.
Computers > Database Management > Data Mining.
Computers > Data Modeling & Design.
Data mining
Social sciences > Statistical methods > Data processing
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Authors
  • About the Reviewer
  • www.PacktPub.com
  • Customer Feedback
  • Dedication
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Data Mining and Predictive Analytics
  • Introduction to data mining
  • CRISP-DM overview
  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment
  • Learning more about CRISP-DM
  • The data mining process (as a case study)
  • Summary
  • Chapter 2: The Basics of Using IBM SPSS Modeler
  • Introducing the Modeler graphic user interface
  • Stream canvas
  • Palettes
  • Modeler menus
  • Toolbar
  • Manager tabs
  • Project window
  • Building streams
  • Mouse buttons
  • Adding nodes
  • Editing nodes
  • Deleting nodes
  • Building a stream
  • Connecting nodes
  • Deleting connections
  • Modeler stream rules
  • Help options
  • Help menu
  • Dialog help
  • Summary
  • Chapter 3: Importing Data into Modeler
  • Data structure
  • Var. File source node
  • Var. File source node File tab
  • Var. File source node Data tab
  • Var. File source node Filter tab
  • Var. File source node Types tab
  • Var. File source node Annotations tab
  • Viewing data
  • Excel source node
  • Database source node
  • Levels of measurement and roles
  • Summary
  • Chapter 4: Data Quality and Exploration
  • Data Audit node options
  • Data Audit node results
  • The Quality tab
  • Missing data
  • Ways to address missing data
  • Defining missing values in the Type node
  • Imputing missing values with the Data Audit node
  • Summary
  • Chapter 5: Cleaning and Selecting Data
  • Selecting cases
  • Expression Builder
  • Sorting cases
  • Identifying and removing duplicate cases
  • Reclassifying categorical values
  • Summary
  • Chapter 6: Combining Data Files
  • Combining data files with the Append node
  • Removing fields with the Filter node.
  • Combining data files with the Merge node
  • The Filter tab
  • The Optimization tab
  • Summary
  • Chapter 7: Deriving New Fields
  • Derive
  • Formula
  • Derive
  • Flag
  • Derive
  • Nominal
  • Derive
  • Conditional
  • Summary
  • Chapter 8: Looking for Relationships Between Fields
  • Relationships between categorical fields
  • Distribution node
  • Matrix node
  • Relationships between categorical and continuous fields
  • Histogram node
  • Means node
  • Relationships between continuous fields
  • Plot node
  • Statistics node
  • Summary
  • Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler
  • Classification
  • Categorical targets
  • Numeric targets
  • The Auto nodes
  • Data reduction modeling nodes
  • Association
  • Segmentation
  • Choosing between models
  • Summary
  • Chapter 10: Decision Tree Models
  • Decision tree theory
  • CHAID theory
  • How CHAID processes different types of input variables
  • Stopping rules
  • Building a CHAID Model
  • Partition node
  • Overfitting
  • CHAID dialog options
  • CHAID results
  • Summary
  • Chapter 11: Model Assessment and Scoring
  • Contrasting model assessment with the Evaluation phase
  • Model assessment using the Analysis node
  • Modifying CHAID settings
  • Model comparison using the Analysis node
  • Model assessment and comparison using the Evaluation node
  • Scoring new data
  • Exporting predictions
  • Summary
  • Index.

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